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A Novel Multi-Scale Feature Fusion-Based 3SCNet for Building Crack Detection

Author

Listed:
  • Dhirendra Prasad Yadav

    (Department of Computer Engineering & Applications, GLA University, Mathura 281406, Uttar Pradesh, India)

  • Kamal Kishore

    (Advanced Construction Engineering Research Center, Department of Civil Engineering, GLA University, Mathura 281406, Uttar Pradesh, India)

  • Ashish Gaur

    (Department of Computer Engineering & Applications, GLA University, Mathura 281406, Uttar Pradesh, India)

  • Ankit Kumar

    (Department of Computer Engineering & Applications, GLA University, Mathura 281406, Uttar Pradesh, India)

  • Kamred Udham Singh

    (Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan
    School of Computing, Graphic Era Hill University, Dehradun 248002, Uttarakhand, India)

  • Teekam Singh

    (School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India)

  • Chetan Swarup

    (Department of Basic Science, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh-Male Campus, Riyadh 13316, Saudi Arabia)

Abstract

Crack detection at an early stage is necessary to save people’s lives and to prevent the collapse of building/bridge structures. Manual crack detection is time-consuming, especially when a building structure is too high. Image processing, machine learning, and deep learning-based methods can be used in such scenarios to build an automatic crack detection system. This study uses a novel deep convolutional neural network, 3SCNet (3ScaleNetwork), for crack detection. The SLIC (Simple Linear Iterative Clustering) segmentation method forms the cluster of similar pixels and the LBP (Local Binary Pattern) finds the texture pattern in the crack image. The SLIC, LBP, and grey images are fed to 3SCNet to form pool of feature vector. This multi-scale feature fusion (3SCNet+LBP+SLIC) method achieved the highest sensitivity, specificity, an accuracy of 99.47%, 99.75%, and 99.69%, respectively, on a public historical building crack dataset. It shows that using SLIC super pixel segmentation and LBP can improve the performance of the CNN (Convolution Neural Network). The achieved performance of the model can be used to develop a real-time crack detection system.

Suggested Citation

  • Dhirendra Prasad Yadav & Kamal Kishore & Ashish Gaur & Ankit Kumar & Kamred Udham Singh & Teekam Singh & Chetan Swarup, 2022. "A Novel Multi-Scale Feature Fusion-Based 3SCNet for Building Crack Detection," Sustainability, MDPI, vol. 14(23), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16179-:d:992955
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